Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 361–370 of 2167 papers

Title	Date	Tasks	Status	Hype
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?	Jan 5, 2025	Image CaptioningImage to text	CodeCode Available	1
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant	Mar 8, 2022	Visual Question Answering (VQA)	CodeCode Available	1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis	Jun 28, 2024	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available	1
FunQA: Towards Surprising Video Comprehension	Jun 26, 2023	Question AnsweringText Generation	CodeCode Available	1
Generative Bias for Robust Visual Question Answering	Aug 1, 2022	Knowledge DistillationQuestion Answering	CodeCode Available	1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture	Jun 16, 2024	DiversityMultiple-choice	CodeCode Available	1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding	Jul 6, 2024	Optical Character Recognition (OCR)Visual Question Answering (VQA)	CodeCode Available	1
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules	May 11, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax	Mar 2, 2023	DescriptiveImage Captioning	CodeCode Available	1
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified